Compile declarative language model calls into self-improving pipelines.
npx skills add https://github.com/zangjeicy/Hermes --skill dspy-zangjeicy
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/zangjeicy/Hermes --skill dspy-zangjeicy
This skill addresses the fragility and manual labor of traditional prompt engineering by providing a framework to program language models declaratively, allowing for systematic, data-driven optimization of prompts and pipelines.
Use the dspy skill to configure a language model and define a signature for a question-answering module.
High-intent search queries and answers about installing and using this skill.
Automatic prompt optimization uses data-driven methods to improve prompts and module performance without manual trial and error. It compiles declarative language model calls into self-improving pipelines by systematically optimizing prompts and few-shot examples.
Declarative programming for AI pipelines involves defining modular components like RAG, agents, and classifiers to create complex, maintainable AI systems. It shifts prompt engineering from fragile manual labor to systematic, data-driven optimization of language model behavior.
Yes, you can build a multi-stage RAG system that automatically optimizes its retrieval and generation prompts. By using a provided training dataset, the framework maximizes accuracy through data-driven optimization of the underlying language model calls.
Yes, this approach supports building modular AI systems across various language model providers. It requires integration with the openai and anthropic libraries to execute and optimize LM programs effectively across different platforms.
To configure a language model and define a signature for question answering, you use the framework to establish a declarative module. This module compiles your specified inputs and outputs into optimized few-shot examples for the LM pipeline.
The limitations of automatic prompt optimization include the requirement to integrate specific libraries like dspy, openai, and anthropic to execute programs. Additionally, users must provide a training dataset to successfully drive the data-driven optimization of prompts.
anilcan-karaAutomate declarative LM programming and self-improvement workflows with DSPy.
NovusAevumDesign production-ready prompts for LLM applications with few-shot and chain-of-thought techniques.
cuoreinpaceImplement RAG systems with vector databases and semantic search for LLM applications.
liuerfireDesigns, optimizes and evaluates prompts for LLMs using techniques like chain-of-thought and few-shot learning.
